In-situ anomaly detection for power MOSFET’s degradation based on unsupervised LSTM-Autoencoder

Abstract

Aiming at the reliability bottlenecks of power MOSFETs in such key application fields as new energy vehicles (NEVs), photovoltaic (PV) inverters, and industrial motor drives, this paper proposes an unsupervised anomaly detection method for power MOSFET degradation based on the Long Short-Term Memory Autoencoder (LSTM-AE). Without relying on labeled data or complex hardware modifications, the method can adaptively learn the health patterns and temporal features of devices, addressing the limitations of traditional methods such as difficulty in capturing long-term dependencies and poor noise immunity. Three different test conditions for power cycling tests were designed to collect operational data of power MOSFET devices. On-state resistance (RDS(on)) and body diode voltage drop(VSD) were selected as input features, and the Leave-One-Out Cross-Validation (LOOCV) method was adopted to verify the model performance. Experimental results demonstrate that the method can effectively capture weak degradation features during the stage of insignificant device performance changes, achieving an early warning prior to device failure. Under 3σ principle threshold, its detection performance outperforms the Multilayer Perceptron Autoencoder (MLP-AE) and traditional Mahalanobis Distance (MD) methods, with the optimal sequence size being 64. It achieves a high accuracy in identifying normal states and exhibits high recognition capability for severely degraded states, providing a robust, efficient, and practical solution for the health monitoring of power MOSFETs.